Recruitment and Retention of International Students in Canada During the Pandemic: An Analysis of Immigration Policy Measures
Bibliographic record
Abstract
This paper examines the immigration policy measures introduced in Canada during the COVID-19 pandemic and analyzes how these measures supported the recruitment and retention of international students. The analysis spans from March 2020, when the pandemic was declared, to December 2022, when most educational institutions began transitioning back to in-person learning. Based on a comprehensive review of policy documents, the study finds federal measures that facilitated the recruitment and retention of international students, including travel regulations, online learning provisions, work-related measures, and pathways to permanent residency. Findings indicate a significant shift in policy-making from reactive to proactive strategies, emphasizing economic recovery as immediate health threats diminished. The pandemic necessitated rapid policy innovation, particularly in online learning and work-related provisions, which may influence future approaches to international education and immigration in Canada. The paper concludes with a discussion on the implications of these policies and recommendations for future research to understand their long-term effects on Canada's international education sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".